Most existing 6D object pose estimation or tracking approaches assume access to an object instance's 3D model [1]}, [2]}. Having access to such instance 3D models complicates generalization to novel, unseen instances. To overcome this limitation, recent efforts have relaxed this assumption and require only category-level 3D models for 6D pose estimation [3]}, [4]}, [5]}, [6]} or tracking [7]}. They often achieve this by training over a large number of CAD models from the same category. While promising results have been demonstrated for previously seen object categories, there are still limitations. These methods are constrained by the variety of categories in the training database. Popular 3D model databases, such as ShapeNet [8]} and ModelNet40 [9]}, contain 55 and 40 categories respectively. This is still far from sufficient to cover diverse object categories present in the real world. Furthermore, 3D model databases often require nontrivial manual effort and expert domain knowledge to build, involving steps such as scanning [10]}, mesh refinement [11]} or CAD design.
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